System and method for processing normalized voice feedback for use in automated patient care

ABSTRACT

A system and method for processing normalized voice feedback for use in automated patient care is described. One or more physiological measures relating to individual patient information and one or more quality of life measures relating to normalized spoken patient self-assessment indicators are retrieved from a patient care record. Each quality of life measure is recorded substantially contemporaneous to the physiological measures. The physiological measures and the contemporaneously recorded quality of life measures retrieved from one such patient care record are analyzed to determine a patient status.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This patent application is a continuation of U.S. patentapplication, Ser. No. 09/476,600, filed Dec. 31, 1999, pending, which isa continuation-in-part of U.S. Pat. No. 6,203,495, issued Mar. 20, 2001,which is a continuation-in-part of U.S. patent application, Ser. No.09/324,894, filed Jun. 3, 1999, pending, the disclosures of which areincorporated by reference.

FIELD OF THE INVENTION

[0002] The present invention relates in general to automated datacollection and analysis, and, in particular, to a system and method forprocessing normalized voice feedback for use in automated patient care.

BACKGROUND OF THE INVENTION

[0003] A broad class of medical subspecialties, including cardiology,endocrinology, hematology, neurology, gastroenterology, urology,ophthalmology, and otolaryngology, to name a few, rely on accurate andtimely patient information for use in aiding health care providers indiagnosing and treating diseases and disorders. Often, proper medicaldiagnosis requires information on physiological events of short durationand sudden onset, yet these types of events are often occur infrequentlyand with little or no warning. Fortunately, such patient information canbe obtained via external, implantable, cutaneous, subcutaneous, andmanual medical devices, and combinations thereof. For example, in thearea of cardiology, implantable pulse generators (IPGs) are medicaldevices commonly used to treat irregular heartbeats, known asarrhythmias. There are three basic types of IPGs. Cardiac pacemakers areused to manage bradycardia, an abnormally slow or irregular heartbeat.Bradycardia can cause symptoms such as fatigue, dizziness, and fainting.Implantable cardioverter defibrillators (ICDs) are used to treattachycardia, heart rhythms that are abnormally fast and lifethreatening. Tachycardia can result in sudden cardiac death (SCD).Finally, implantable cardiovascular monitors and therapeutic devices areused to monitor and treat structural problems of the heart, such ascongestive heart failure, as well as rhythm problems.

[0004] Pacemakers and ICDs, as well as other types of implantable andexternal medical devices, are equipped with an on-board, volatile memoryin which telemetered signals can be stored for later retrieval andanalysis. In addition, a growing class of cardiac medical devices,including implantable heart failure monitors, implantable eventmonitors, cardiovascular monitors, and therapy devices, are being usedto provide similar stored device information. These devices are able tostore more than thirty minutes of per heartbeat data. Typically, thetelemetered signals can provide patient device information recorded on aper heartbeat, binned average basis, or derived basis from, for example,atrial electrical activity, ventricular electrical activity, minuteventilation, patient activity score, cardiac output score, mixed venousoxygen score, cardiovascular pressure measures, time of day, and anyinterventions and the relative success of such interventions. Inaddition, many such devices can have multiple sensors, or severaldevices can work together, for monitoring different sites within apatient's body.

[0005] Presently, stored device information is retrieved using aproprietary interrogator or programmer, often during a clinic visit orfollowing a device event. The volume of data retrieved from a singledevice interrogation “snapshot” can be large and proper interpretationand analysis can require significant physician time and detailedsubspecialty knowledge, particularly by cardiologists and cardiacelectrophysiologists. The sequential logging and analysis of regularlyscheduled interrogations can create an opportunity for recognizingsubtle and incremental changes in patient condition otherwiseundetectable by inspection of a single “snapshot.” However, presentapproaches to data interpretation and understanding and practicallimitations on time and physician availability make such analysisimpracticable.

[0006] Similarly, the determination and analysis of the quality of lifeissues which typically accompany the onset of a chronic yet stablediseases, such as coronary-artery disease, is a crucial adjunct toassessing patient wellness and progress. However, unlike in atraditional clinical setting, physicians participating in providingremote patient care are not able to interact with their patients inperson. Consequently, quality of life measures, such as how the patientsubjectively looks and feels, whether the patient has shortness ofbreath, can work, can sleep, is depressed, is sexually active, canperform activities of daily life, and so on, cannot be implicitlygathered and evaluated.

[0007] A prior art system for collecting and analyzing pacemaker and ICDtelemetered signals in a clinical or office setting is the Model 9790Programmer, manufactured by Medtronic, Inc., Minneapolis, Minn. Thisprogrammer can be used to retrieve data, such as patientelectrocardiogram and any measured physiological conditions, collectedby the IPG for recordation, display and printing. The retrieved data isdisplayed in chronological order and analyzed by a physician. Comparableprior art systems are available from other IPG manufacturers, such asthe Model 2901 Programmer Recorder Monitor, manufactured by GuidantCorporation, Indianapolis, Ind., which includes a removable floppydiskette mechanism for patient data storage. These prior art systemslack remote communications facilities and must be operated with thepatient present. These systems present a limited analysis of thecollected data based on a single device interrogation and lack thecapability to recognize trends in the data spanning multiple episodesover time or relative to a disease specific peer group.

[0008] A prior art system for locating and communicating with a remotemedical device implanted in an ambulatory patient is disclosed in U.S.Pat. No. 5,752,976 ('976). The implanted device includes a telemetrytransceiver for communicating data and operating instructions betweenthe implanted device and an external patient communications device. Thecommunications device includes a communication link to a remote medicalsupport network, a global positioning satellite receiver, and a patientactivated link for permitting patient initiated communication with themedical support network. Patient voice communications through thepatient link include both actual patient voice and manually actuatedsignaling which may convey an emergency situation. The patient voice isconverted to an audio signal, digitized, encoded, and transmitted bydata bus to a system controller.

[0009] Related prior art systems for remotely communicating with andreceiving telemetered signals from a medical device are disclosed inU.S. Pat. Nos. 5,113,869 ('869) and 5,336,245 ('245). In the '869patent, an implanted AECG monitor can be automatically interrogated atpreset times of day to telemeter out accumulated data to a telephoniccommunicator or a full disclosure recorder. The communicator can beautomatically triggered to establish a telephonic communication link andtransmit the accumulated data to an office or clinic through a modem. Inthe '245 patent, telemetered data is downloaded to a larger capacity,external data recorder and is forwarded to a clinic using an auto-dialerand fax modem operating in a personal computer-basedprogrammer/interrogator. However, the '976 telemetry transceiver, '869communicator, and '245 programmer/interrogator are limited tofacilitating communication and transferal of downloaded patient data anddo not include an ability to automatically track, recognize, and analyzetrends in the data itself. Moreover, the '976 telemetry transceiverfacilitates patient voice communications through transmission of adigitized audio signal and does not perform voice recognition or otherprocessing to the patient's voice.

[0010] In addition, the uses of multiple sensors situated within apatient's body at multiple sites are disclosed in U.S. Pat. No.5,040,536 ('536) and U.S. Pat. No. 5,987,352 ('352). In the '536 patent,an intravascular pressure posture detector includes at least twopressure sensors implanted in different places in the cardiovascularsystem, such that differences in pressure with changes in posture aredifferentially measurable. However, the physiological measurements areused locally within the device, or in conjunction with any implantabledevice, to effect a therapeutic treatment. In the '352 patent, an eventmonitor can include additional sensors for monitoring and recordingphysiological signals during arrhythmia and syncopal events. Therecorded signals can be used for diagnosis, research or therapeuticstudy, although no systematic approach to analyzing these signals,particularly with respect to peer and general population groups, ispresented.

[0011] Thus, there is a need for a system and method for providingcontinuous retrieval, transferal, and automated analysis of retrievedmedical device information, such as telemetered signals, retrieved ingeneral from a broad class of implantable and external medical devices.Preferably, the automated analysis would include recognizing a trendindicating disease onset, progression, regression, and status quo anddetermining whether medical intervention is necessary.

[0012] There is a further need for a system and method that would allowconsideration of sets of collected measures, both actual and derived,from multiple device interrogations. These collected measures sets couldthen be compared and analyzed against short and long term periods ofobservation.

[0013] There is a further need for a system and method that would enablethe measures sets for an individual patient to be self-referenced andcross-referenced to similar or dissimilar patients and to the generalpatient population. Preferably, the historical collected measures setsof an individual patient could be compared and analyzed against those ofother patients in general or of a disease specific peer group inparticular.

[0014] There is a further need for a system and method for accepting andnormalizing live voice feedback spoken by an individual patient while anidentifiable set of telemetered signals is collected by a implantablemedical device. Preferably, the normalized voice feedback asemi-quantitative self-assessment of an individual patient's physicaland emotional well being at a time substantially contemporaneous to thecollection of the telemetered signals.

SUMMARY OF THE INVENTION

[0015] The present invention provides a system and method for automatedcollection and analysis of patient information retrieved from animplantable medical device for remote patient care. The patient deviceinformation relates to individual measures recorded by and retrievedfrom implantable medical devices, such as IPGs and monitors. The patientdevice information is received on a regular, e.g., daily, basis as setsof collected measures which are stored along with other patient recordsin a database. The information can be analyzed in an automated fashionand feedback provided to the patient at any time and in any location.

[0016] The present invention also provides a system and method forproviding normalized voice feedback from an individual patient in anautomated collection and analysis patient care system. As before,patient device information is received on a regular, e.g., daily, basisas sets of collected measures which are stored along with other patientrecords in a database. Voice feedback spoken by an individual patient isprocessed into a set of quality of life measures by a remote clientsubstantially contemporaneous to the recordation of an identifiable setof collected device measures by the implantable medical device. Theprocessed voice feedback and identifiable collected device measures setare both received and stored into the patient record in the database forsubsequent evaluation.

[0017] An embodiment of the present invention is a system and method forprocessing normalized voice feedback for use in automated patient care.One or more physiological measures relating to individual patientinformation and one or more quality of life measures relating tonormalized spoken patient self-assessment indicators are retrieved froma patient care record. Each quality of life measure is recordedsubstantially contemporaneous to the physiological measures. Thephysiological measures and the contemporaneously recorded quality oflife measures retrieved from one such patient care record are analyzedto determine a patient status.

[0018] A further embodiment of the present invention is a system andmethod for soliciting normalized voice feedback for use in automatedpatient care. One or more physiological measures relating to individualpatient information are obtained from a medical device having a sensorfor monitoring and recording from an anatomical site at least one ofdirectly and derivatively. One or more quality of life measures relatingto normalized spoken patient self-assessment indicators are recorded.Each quality of life measure is recorded substantially contemporaneousto the physiological measures. The physiological measures and thequality of life measures are stored in patient care records.

[0019] The present invention facilitates the gathering, storage, andanalysis of critical patient information obtained on a routine basis andanalyzed in an automated manner. Thus, the burden on physicians andtrained personnel to evaluate the volumes of information issignificantly minimized while the benefits to patients are greatlyenhanced.

[0020] The present invention also enables the simultaneous collection ofboth physiological measures from implantable medical devices and qualityof life measures spoken in the patient's own words. Voice recognitiontechnology enables the spoken patient feedback to be normalized to astandardized set of semi-quantitative quality of life measures, therebyfacilitating holistic remote, automated patient care.

[0021] Still other embodiments of the present invention will becomereadily apparent to those skilled in the art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

[0022]FIG. 1 is a block diagram showing a system for automatedcollection and analysis of patient information retrieved from animplantable medical device for remote patient care in accordance withthe present invention;

[0023]FIG. 2 is a block diagram showing the hardware components of theserver system of the system of FIG. 1;

[0024]FIG. 3 is a block diagram showing the software modules of theserver system of the system of FIG. 1;

[0025]FIG. 4 is a block diagram showing the analysis module of theserver system of FIG. 3;

[0026]FIG. 5 is a database schema showing, by way of example, theorganization of a cardiac patient care record stored in the database ofthe system of FIG. 1;

[0027]FIG. 6 is a record view showing, by way of example, a set ofpartial cardiac patient care records stored in the database of thesystem of FIG. 1;

[0028]FIG. 7 is a flow diagram showing a method for automated collectionand analysis of patient information retrieved from an implantablemedical device for remote patient care in accordance with the presentinvention;

[0029]FIG. 8 is a flow diagram showing a routine for analyzing collectedmeasures sets for use in the method of FIG. 7;

[0030]FIG. 9 is a flow diagram showing a routine for comparing siblingcollected measures sets for use in the routine of FIG. 8;

[0031]FIGS. 10A and 10B are flow diagrams showing a routine forcomparing peer collected measures sets for use in the routine of FIG. 8;

[0032]FIG. 11 is a flow diagram showing a routine for providing feedbackfor use in the method of FIG. 7;

[0033]FIG. 12 is a block diagram showing a system for providingnormalized voice feedback from an individual patient in an automatedcollection and analysis patient care system;

[0034]FIG. 13 is a block diagram showing the software modules of theremote client of the system of FIG. 12;

[0035]FIG. 14 is a block diagram showing the software modules of theserver system of the system of FIG. 12;

[0036]FIG. 15 is a database schema showing, by way of example, theorganization of a quality of life record for cardiac patient care storedas part of a patient care record in the database of the system of FIG.12;

[0037] FIGS. 16A-16B are flow diagrams showing a method for providingnormalized voice feedback from an individual patient in an automatedcollection and analysis patient care system;

[0038]FIG. 17 is a flow diagram showing a routine for processing voicefeedback for use in the method of FIGS. 16A-16B;

[0039]FIG. 18 is a flow diagram showing a routine for requesting aquality of life measure for use in the routine of FIG. 17;

[0040]FIG. 19 is a flow diagram showing a routine for recognizing andtranslating individual spoken words for use in the routine of FIG. 17;

[0041]FIG. 20 is a block diagram showing the software modules of theserver system in a further embodiment of the system of FIG. 12;

[0042]FIG. 21 is a block diagram showing a system for providingnormalized voice feedback from an individual patient in an automatedcollection and analysis patient care system in accordance with a furtherembodiment of the present invention;

[0043]FIG. 22 is a block diagram showing the analysis module of theserver system of FIG. 21;

[0044]FIG. 23 is a database schema showing, by way of example, theorganization of a quality of life and symptom measures set record forcare of patients stored as part of a patient care record in the databaseof the system of FIG. 21;

[0045]FIG. 24 is a record view showing, by way of example, a set ofpartial cardiac patient care records stored in the database of thesystem of FIG. 21;

[0046]FIG. 25 is a Venn diagram showing, by way of example, peer groupoverlap between the partial patient care records of FIG. 24; and

[0047] FIGS. 26A-26B are flow diagrams showing a method for providingnormalized voice feedback from an individual patient in an automatedcollection and analysis patient care system in accordance with a furtherembodiment of the present invention.

DETAILED DESCRIPTION

[0048]FIG. 1 is a block diagram showing a system 10 for automatedcollection and analysis of patient information retrieved from animplantable medical device for remote patient care in accordance withthe present invention. A patient 11 is a recipient of an implantablemedical device 12, such as, by way of example, an IPG or a heart failureor event monitor, with a set of leads extending into his or her heart.The implantable medical device 12 includes circuitry for recording intoa short-term, volatile memory telemetered signals, which are stored as aset of collected measures for later retrieval.

[0049] For an exemplary cardiac implantable medical device, thetelemetered signals non-exclusively present patient information recordedon a per heartbeat, binned average or derived basis and relating to:atrial electrical activity, ventricular electrical activity, minuteventilation, patient activity score, cardiac output score, mixed venousoxygenation score, cardiovascular pressure measures, time of day, thenumber and types of interventions made, and the relative success of anyinterventions, plus the status of the batteries and programmed settings.Examples of pacemakers suitable for use in the present invention includethe Discovery line of pacemakers, manufactured by Guidant Corporation,Indianapolis, Ind. Examples of ICDs suitable for use in the presentinvention include the Gem line of ICDs, manufactured by MedtronicCorporation, Minneapolis, Minn.

[0050] In the described embodiment, the patient 11 has a cardiacimplantable medical device. However, a wide range of related implantablemedical devices are used in other areas of medicine and a growing numberof these devices are also capable of measuring and recording patientinformation for later retrieval. These implantable medical devicesinclude monitoring and therapeutic devices for use in metabolism,endocrinology, hematology, neurology, muscular disorders,gastroenterology, urology, ophthalmology, otolaryngology, orthopedics,and similar medical subspecialties. One skilled in the art would readilyrecognize the applicability of the present invention to these relatedimplantable medical devices.

[0051] On a regular basis, the telemetered signals stored in theimplantable medical device 12 are retrieved. By way of example, aprogrammer 14 can be used to retrieve the telemetered signals. However,any form of programmer, interrogator, recorder, monitor, or telemeteredsignals transceiver suitable for communicating with an implantablemedical device 12 could be used, as is known in the art. In addition, apersonal computer or digital data processor could be interfaced to theimplantable medical device 12, either directly or via a telemeteredsignals transceiver configured to communicate with the implantablemedical device 12.

[0052] Using the programmer 14, a magnetized reed switch (not shown)within the implantable medical device 12 closes in response to theplacement of a wand 13 over the location of the implantable medicaldevice 12. The programmer 14 communicates with the implantable medicaldevice 12 via RF signals exchanged through the wand 13. Programming orinterrogating instructions are sent to the implantable medical device 12and the stored telemetered signals are downloaded into the programmer14. Once downloaded, the telemetered signals are sent via aninternetwork 15, such as the Internet, to a server system 16 whichperiodically receives and stores the telemetered signals in a database17, as further described below with reference to FIG. 2.

[0053] An example of a programmer 14 suitable for use in the presentinvention is the Model 2901 Programmer Recorder Monitor, manufactured byGuidant Corporation, Indianapolis, Ind., which includes the capabilityto store retrieved telemetered signals on a proprietary removable floppydiskette. The telemetered signals could later be electronicallytransferred using a personal computer or similar processing device tothe internetwork 15, as is known in the art.

[0054] Other alternate telemetered signals transfer means could also beemployed. For instance, the stored telemetered signals could beretrieved from the implantable medical device 12 and electronicallytransferred to the internetwork 15 using the combination of a remoteexternal programmer and analyzer and a remote telephonic communicator,such as described in U.S. Pat. No. 5,113,869, the disclosure of which isincorporated herein by reference. Similarly, the stored telemeteredsignals could be retrieved and remotely downloaded to the server system16 using a world-wide patient location and data telemetry system, suchas described in U.S. Pat. No. 5,752,976, the disclosure of which isincorporated herein by reference.

[0055] The received telemetered signals are analyzed by the serversystem 16, which generates a patient status indicator. The feedback isthen provided back to the patient 11 through a variety of means. By wayof example, the feedback can be sent as an electronic mail messagegenerated automatically by the server system 16 for transmission overthe internetwork 15. The electronic mail message is received by a remoteclient 18, such as a personal computer (PC), situated for local accessby the patient 11. Alternatively, the feedback can be sent through atelephone interface device 19 as an automated voice mail message to atelephone 21 or as an automated facsimile message to a facsimile machine22, both also situated for local access by the patient 11. In additionto a remote client 18, telephone 21, and facsimile machine 22, feedbackcould be sent to other related devices, including a network computer,wireless computer, personal data assistant, television, or digital dataprocessor. Preferably, the feedback is provided in a tiered fashion, asfurther described below with reference to FIG. 3.

[0056]FIG. 2 is a block diagram showing the hardware components of theserver system 16 of the system 10 of FIG. 1. The server system 16consists of three individual servers: network server 31, database server34, and application server 35. These servers are interconnected via anintranetwork 33. In the described embodiment, the functionality of theserver system 16 is distributed among these three servers for efficiencyand processing speed, although the functionality could also be performedby a single server or cluster of servers. The network server 31 is theprimary interface of the server system 16 onto the internetwork 15. Thenetwork server 31 periodically receives the collected telemeteredsignals sent by remote implantable medical devices over the internetwork15. The network server 31 is interfaced to the internetwork 15 through arouter 32. To ensure reliable data exchange, the network server 31implements a TCP/IP protocol stack, although other forms of networkprotocol stacks are suitable.

[0057] The database server 34 organizes the patient care records in thedatabase 17 and provides storage of and access to information held inthose records. A high volume of data in the form of collected measuressets from individual patients is received. The database server 34 freesthe network server 31 from having to categorize and store the individualcollected measures sets in the appropriate patient care record.

[0058] The application server 35 operates management applications andperforms data analysis of the patient care records, as further describedbelow with reference to FIG. 3. The application server 35 communicatesfeedback to the individual patients either through electronic mail sentback over the internetwork 15 via the network server 31 or as automatedvoice mail or facsimile messages through the telephone interface device19.

[0059] The server system 16 also includes a plurality of individualworkstations 36 (WS) interconnected to the intranetwork 33, some ofwhich can include peripheral devices, such as a printer 37. Theworkstations 36 are for use by the data management and programmingstaff, nursing staff, office staff, and other consultants and authorizedpersonnel.

[0060] The database 17 consists of a high-capacity storage mediumconfigured to store individual patient care records and related healthcare information. Preferably, the database 17 is configured as a set ofhigh-speed, high capacity hard drives, such as organized into aRedundant Array of Inexpensive Disks (RAID) volume. However, any form ofvolatile storage, non-volatile storage, removable storage, fixedstorage, random access storage, sequential access storage, permanentstorage, erasable storage, and the like would be equally suitable. Theorganization of the database 17 is further described below withreference to FIG. 3.

[0061] The individual servers and workstations are general purpose,programmed digital computing devices consisting of a central processingunit (CPU), random access memory (RAM), non-volatile secondary storage,such as a hard drive or CD ROM drive, network interfaces, and peripheraldevices, including user interfacing means, such as a keyboard anddisplay. Program code, including software programs, and data are loadedinto the RAM for execution and processing by the CPU and results aregenerated for display, output, transmittal, or storage. In the describedembodiment, the individual servers are Intel Pentium-based serversystems, such as available from Dell Computers, Austin, Tex., or CompaqComputers, Houston, Tex. Each system is preferably equipped with 128 MBRAM, 100 GB hard drive capacity, data backup facilities, and relatedhardware for interconnection to the intranetwork 33 and internetwork 15.In addition, the workstations 36 are also Intel Pentium-based personalcomputer or workstation systems, also available from Dell Computers,Austin, Tex., or Compaq Computers, Houston, Tex. Each workstation ispreferably equipped with 64 MB RAM, 10 GB hard drive capacity, andrelated hardware for interconnection to the intranetwork 33. Other typesof server and workstation systems, including personal computers,minicomputers, mainframe computers, supercomputers, parallel computers,workstations, digital data processors and the like would be equallysuitable, as is known in the art.

[0062] The telemetered signals are communicated over an internetwork 15,such as the Internet. However, any type of electronic communicationslink could be used, including an intranetwork link, serial link, datatelephone link, satellite link, radio-frequency link, infrared link,fiber optic link, coaxial cable link, television link, and the like, asis known in the art. Also, the network server 31 is interfaced to theinternetwork 15 using a T-1 network router 32, such as manufactured byCisco Systems, Inc., San Jose, Calif. However, any type of interfacingdevice suitable for interconnecting a server to a network could be used,including a data modem, cable modem, network interface, serialconnection, data port, hub, frame relay, digital PBX, and the like, asis known in the art.

[0063]FIG. 3 is a block diagram showing the software modules of theserver system 16 of the system 10 of FIG. 1. Each module is a computerprogram written as source code in a conventional programming language,such as the C or Java programming languages, and is presented forexecution by the CPU as object or byte code, as is known in the arts.The various implementations of the source code and object and byte codescan be held on a computer-readable storage medium or embodied on atransmission medium in a carrier wave. There are three basic softwaremodules, which functionally define the primary operations performed bythe server system 16: database module 51, analysis module 53, andfeedback module 55. In the described embodiment, these modules areexecuted in a distributed computing environment, although a singleserver or a cluster of servers could also perform the functionality ofthe modules. The module functions are further described below in moredetail beginning with reference to FIG. 7.

[0064] For each patient being provided remote patient care, the serversystem 16 periodically receives a collected measures set 50 which isforwarded to the database module 51 for processing. The database module51 organizes the individual patent care records stored in the database52 and provides the facilities for efficiently storing and accessing thecollected measures sets 50 and patient data maintained in those records.An exemplary database schema for use in storing collected measures sets50 in a patient care record is described below, by way of example, withreference to FIG. 5. The database server 34 (shown in FIG. 2) performsthe functionality of the database module 51. Any type of databaseorganization could be utilized, including a flat file system,hierarchical database, relational database, or distributed database,such as provided by database vendors, such as Oracle Corporation,Redwood Shores, Calif.

[0065] The analysis module 53 analyzes the collected measures sets 50stored in the patient care records in the database 52. The analysismodule 53 makes an automated determination of patient wellness in theform of a patient status indicator 54. Collected measures sets 50 areperiodically received from implantable medical devices and maintained bythe database module 51 in the database 52. Through the use of thiscollected information, the analysis module 53 can continuously followthe medical well being of a patient and can recognize any trends in thecollected information that might warrant medical intervention. Theanalysis module 53 compares individual measures and derived measuresobtained from both the care records for the individual patient and thecare records for a disease specific group of patients or the patientpopulation in general. The analytic operations performed by the analysismodule 53 are further described below with reference to FIG. 4. Theapplication server 35 (shown in FIG. 2) performs the functionality ofthe analysis module 53.

[0066] The feedback module 55 provides automated feedback to theindividual patient based, in part, on the patient status indicator 54.As described above, the feedback could be by electronic mail or byautomated voice mail or facsimile. Preferably, the feedback is providedin a tiered manner. In the described embodiment, four levels ofautomated feedback are provided. At a first level, an interpretation ofthe patient status indicator 54 is provided. At a second level, anotification of potential medical concern based on the patient statusindicator 54 is provided. This feedback level could also be coupled withhuman contact by specially trained technicians or medical personnel. Ata third level, the notification of potential medical concern isforwarded to medical practitioners located in the patient's geographicarea. Finally, at a fourth level, a set of reprogramming instructionsbased on the patient status indicator 54 could be transmitted directlyto the implantable medical device to modify the programming instructionscontained therein. As is customary in the medical arts, the basic tieredfeedback scheme would be modified in the event of bona fide medicalemergency. The application server 35 (shown in FIG. 2) performs thefunctionality of the feedback module 55.

[0067]FIG. 4 is a block diagram showing the analysis module 53 of theserver system 16 of FIG. 3. The analysis module 53 contains twofunctional submodules: comparison module 62 and derivation module 63.The purpose of the comparison module 62 is to compare two or moreindividual measures, either collected or derived. The purpose of thederivation module 63 is to determine a derived measure based on one ormore collected measures which is then used by the comparison module 62.For instance, a new and improved indicator of impending heart failurecould be derived based on the exemplary cardiac collected measures setdescribed with reference to FIG. 5. The analysis module 53 can operateeither in a batch mode of operation wherein patient status indicatorsare generated for a set of individual patients or in a dynamic modewherein a patient status indicator is generated on the fly for anindividual patient.

[0068] The comparison module 62 receives as inputs from the database 17two input sets functionally defined as peer collected measures sets 60and sibling collected measures sets 61, although in practice, thecollected measures sets are stored on a per sampling basis. Peercollected measures sets 60 contain individual collected measures setsthat all relate to the same type of patient information, for instance,atrial electrical activity, but which have been periodically collectedover time. Sibling collected measures sets 61 contain individualcollected measures sets that relate to different types of patientinformation, but which may have been collected at the same time ordifferent times. In practice, the collected measures sets are notseparately stored as “peer” and “sibling” measures. Rather, eachindividual patient care record stores multiple sets of sibling collectedmeasures. The distinction between peer collected measures sets 60 andsibling collected measures sets 61 is further described below withreference to FIG. 6.

[0069] The derivation module 63 determines derived measures sets 64 onan as-needed basis in response to requests from the comparison module62. The derived measures 64 are determined by performing linear andnon-linear mathematical operations on selected peer measures 60 andsibling measures 61, as is known in the art.

[0070]FIG. 5 is a database schema showing, by way of example, theorganization of a cardiac patient care record stored 70 in the database17 of the system 10 of FIG. 1. Only the information pertaining tocollected measures sets are shown. Each patient care record would alsocontain normal identifying and treatment profile information, as well asmedical history and other pertinent data (not shown). Each patient carerecord stores a multitude of collected measures sets for an individualpatient. Each individual set represents a recorded snapshot oftelemetered signals data which was recorded, for instance, per heartbeator binned average basis by the implantable medical device 12. Forexample, for a cardiac patient, the following information would berecorded as a collected measures set: atrial electrical activity 71,ventricular electrical activity 72, time of day 73, activity level 74,cardiac output 75, oxygen level 76, cardiovascular pressure measures 77,pulmonary measures 78, interventions made by the implantable medicaldevice 78, and the relative success of any interventions made 80. Inaddition, the implantable medical device 12 would also communicatedevice specific information, including battery status 81 and programsettings 82. Other types of collected measures are possible. Inaddition, a well-documented set of derived measures can be determinedbased on the collected measures, as is known in the art.

[0071]FIG. 6 is a record view showing, by way of example, a set ofpartial cardiac patient care records stored in the database 17 of thesystem 10 of FIG. 1. Three patient care records are shown for Patient 1,Patient 2, and Patient 3. For each patent, three sets of measures areshown, X, Y, and Z. The measures are organized into sets with Set 0representing sibling measures made at a reference time t=0. Similarly,Set n−2, Set n−1 and Set n each represent sibling measures made at laterreference times t=n−2, t=n−1 and t=n, respectively.

[0072] For a given patient, for instance, Patient 1, all measuresrepresenting the same type of patient information, such as measure X,are peer measures. These are measures, which are monitored over time ina disease-matched peer group. All measures representing different typesof patient information, such as measures X, Y, and Z are siblingmeasures. These are measures which are also measured over time, butwhich might have medically significant meaning when compared to eachother within a single set. Each of the measures, X, Y and Z, could beeither collected or derived measures.

[0073] The analysis module 53 (shown in FIG. 4) performs two basic formsof comparison. First, individual measures for a given patient can becompared to other individual measures for that same patient. Thesecomparisons might be peer-to-peer measures projected over time, forinstance, X_(n), X_(n−1), X_(n−2), . . . X₀, or sibling-to-siblingmeasures for a single snapshot, for instance, X_(n), Y_(n), and Z_(n),or projected over time, for instance, X_(n), Y_(n), Z_(n), X_(n−1),Y_(n−1), Z_(n−1), X_(n−2), Y_(n−2), Z_(n−2), . . . X₀, Y₀, Z₀. Second,individual measures for a given patient can be compared to otherindividual measures for a group of other patients sharing the samedisease-specific characteristics or to the patient population ingeneral. Again, these comparisons might be peer-to-peer measuresprojected over time, for instance, X_(n), X_(n′), X_(n″), X_(n−1),X_(n−1′), X_(n−1″), X_(n−2), X_(n−2′), X_(n−2″), . . . X₀, X_(0′),X_(0″), or comparing the individual patient's measures to an averagefrom the group. Similarly, these comparisons might be sibling-to-siblingmeasures for single snapshots, for instance, X_(n), X_(n′), X_(n″),Y_(n), Y_(n′), Y_(n″), and Z_(n), Z_(n′), Z_(n″), or projected overtime, for instance, X_(n), X_(n′), X_(n″), Y_(n), Y_(n′), Y_(n″), Z_(n),Z_(n′), Z_(n″), X_(n−1), X_(n−1′), X_(n−1″), Y_(n−1), Y_(n−1′),Y_(n−1″), Z_(n−1), Z_(n−1′), Z_(n−1″), X_(n−2), X_(n−2′), X_(n−2″),Y_(n−2), Y_(n−2′), Y_(n−2″), Z_(n−2), Z_(n−2′), Z_(n−2″), . . . X₀,X_(0′), X_(0″), Y₀, Y_(0′), Y_(0″), and Z₀, Z_(0′), Z_(0″). Other formsof comparisons are feasible.

[0074]FIG. 7 is a flow diagram showing a method 90 for automatedcollection and analysis of patient information retrieved from animplantable medical device 12 for remote patient care in accordance withthe present invention. The method 90 is implemented as a conventionalcomputer program for execution by the server system 16 (shown in FIG.1). As a preparatory step, the patient care records are organized in thedatabase 17 with a unique patient care record assigned to eachindividual patient (block 91). Next, the collected measures sets for anindividual patient are retrieved from the implantable medical device 12(block 92) using a programmer, interrogator, telemetered signalstransceiver, and the like. The retrieved collected measures sets aresent, on a substantially regular basis, over the internetwork 15 orsimilar communications link (block 93) and periodically received by theserver system 16 (block 94). The collected measures sets are stored intothe patient care record in the database 17 for that individual patient(block 95). One or more of the collected measures sets for that patientare analyzed (block 96), as further described below with reference toFIG. 8. Finally, feedback based on the analysis is sent to that patientover the internetwork 15 as an email message, via telephone line as anautomated voice mail or facsimile message, or by similar feedbackcommunications link (block 97), as further described below withreference to FIG. 11.

[0075]FIG. 8 is a flow diagram showing the routine for analyzingcollected measures sets 96 for use in the method of FIG. 7. The purposeof this routine is to make a determination of general patient wellnessbased on comparisons and heuristic trends analyses of the measures, bothcollected and derived, in the patient care records in the database 17. Afirst collected measures set is selected from a patient care record inthe database 17 (block 100). If the measures comparison is to be made toother measures originating from the patient care record for the sameindividual patient (block 101), a second collected measures set isselected from that patient care record (block 102). Otherwise, a groupmeasures comparison is being made (block 101) and a second collectedmeasures set is selected from another patient care record in thedatabase 17 (block 103). Note the second collected measures set couldalso contain averaged measures for a group of disease specific patientsor for the patient population in general.

[0076] Next, if a sibling measures comparison is to be made (block 104),a routine for comparing sibling collected measures sets is performed(block 105), as further described below with reference to FIG. 9.Similarly, if a peer measures comparison is to be made (block 106), aroutine for comparing sibling collected measures sets is performed(block 107), as further described below with reference to FIGS. 10A and10B.

[0077] Finally, a patient status indicator is generated (block 108). Byway of example, cardiac output could ordinarily be approximately 5.0liters per minute with a standard deviation of ±1.0. An actionablemedical phenomenon could occur when the cardiac output of a patient is±3.0-4.0 standard deviations out of the norm. A comparison of thecardiac output measures 75 (shown in FIG. 5) for an individual patientagainst previous cardiac output measures 75 would establish the presenceof any type of downward health trend as to the particular patient. Acomparison of the cardiac output measures 75 of the particular patientto the cardiac output measures 75 of a group of patients would establishwhether the patient is trending out of the norm. From this type ofanalysis, the analysis module 53 generates a patient status indicator 54and other metrics of patient wellness, as is known in the art.

[0078]FIG. 9 is a flow diagram showing the routine for comparing siblingcollected measures sets 105 for use in the routine of FIG. 8. Siblingmeasures originate from the patient care records for an individualpatient. The purpose of this routine is either to compare siblingderived measures to sibling derived measures (blocks 111-113) or siblingcollected measures to sibling collected measures (blocks 115-117). Thus,if derived measures are being compared (block 110), measures areselected from each collected measures set (block 111). First and secondderived measures are derived from the selected measures (block 112)using the derivation module 63 (shown in FIG. 4). The first and secondderived measures are then compared (block 113) using the comparisonmodule 62 (also shown in FIG. 4). The steps of selecting, determining,and comparing (blocks 111-113) are repeated until no further comparisonsare required (block 114), whereupon the routine returns.

[0079] If collected measures are being compared (block 110), measuresare selected from each collected measures set (block 115). The first andsecond collected measures are then compared (block 116) using thecomparison module 62 (also shown in FIG. 4). The steps of selecting andcomparing (blocks 115-116) are repeated until no further comparisons arerequired (block 117), whereupon the routine returns.

[0080]FIGS. 10A and 10B are a flow diagram showing the routine forcomparing peer collected measures sets 107 for use in the routine ofFIG. 8. Peer measures originate from patient care records for differentpatients, including groups of disease specific patients or the patientpopulation in general. The purpose of this routine is to compare peerderived measures to peer derived measures (blocks 122-125), peer derivedmeasures to peer collected measures (blocks 126-129), peer collectedmeasures to peer derived measures (block 131-134), or peer collectedmeasures to peer collected measures (blocks 135-137). Thus, if the firstmeasure being compared is a derived measure (block 120) and the secondmeasure being compared is also a derived measure (block 121), measuresare selected from each collected measures set (block 122). First andsecond derived measures are derived from the selected measures (block123) using the derivation module 63 (shown in FIG. 4). The first andsecond derived measures are then compared (block 124) using thecomparison module 62 (also shown in FIG. 4). The steps of selecting,determining, and comparing (blocks 122-124) are repeated until nofurther comparisons are required (block 115), whereupon the routinereturns.

[0081] If the first measure being compared is a derived measure (block120) but the second measure being compared is a collected measure (block121), a first measure is selected from the first collected measures set(block 126). A first derived measure is derived from the first selectedmeasure (block 127) using the derivation module 63 (shown in FIG. 4).The first derived and second collected measures are then compared (block128) using the comparison module 62 (also shown in FIG. 4). The steps ofselecting, determining, and comparing (blocks 126-128) are repeateduntil no further comparisons are required (block 129), whereupon theroutine returns.

[0082] If the first measure being compared is a collected measure (block120) but the second measure being compared is a derived measure (block130), a second measure is selected from the second collected measuresset (block 131). A second derived measure is derived from the secondselected measure (block 132) using the derivation module 63 (shown inFIG. 4). The first collected and second derived measures are thencompared (block 133) using the comparison module 62 (also shown in FIG.4). The steps of selecting, determining, and comparing (blocks 131-133)are repeated until no further comparisons are required (block 134),whereupon the routine returns.

[0083] If the first measure being compared is a collected measure (block120) and the second measure being compared is also a collected measure(block 130), measures are selected from each collected measures set(block 135). The first and second collected measures are then compared(block 136) using the comparison module 62 (also shown in FIG. 4). Thesteps of selecting and comparing (blocks 135-136) are repeated until nofurther comparisons are required (block 137), whereupon the routinereturns.

[0084]FIG. 11 is a flow diagram showing the routine for providingfeedback 97 for use in the method of FIG. 7. The purpose of this routineis to provide tiered feedback based on the patient status indicator.Four levels of feedback are provided with increasing levels of patientinvolvement and medical care intervention. At a first level (block 150),an interpretation of the patient status indicator 54, preferably phrasedin lay terminology, and related health care information is sent to theindividual patient (block 151) using the feedback module 55 (shown inFIG. 3). At a second level (block 152), a notification of potentialmedical concern, based on the analysis and heuristic trends analysis, issent to the individual patient (block 153) using the feedback module 55.At a third level (block 154), the notification of potential medicalconcern is forwarded to the physician responsible for the individualpatient or similar health care professionals (block 155) using thefeedback module 55. Finally, at a fourth level (block 156),reprogramming instructions are sent to the implantable medical device 12(block 157) using the feedback module 55.

[0085]FIG. 12 is a block diagram showing a system 200 for providingnormalized voice feedback from an individual patient 11 in an automatedcollection and analysis patient care system, such as the system 10 ofFIG. 1. The remote client 18 includes a microphone 201 and a speaker 202which is interfaced internally within the remote client 18 to soundrecordation and reproduction hardware. The patient 11 provides spokenfeedback into the microphone 201 in response to voice prompts reproducedby the remote client 18 on the speaker 202, as further described belowwith reference to FIG. 13. The raw spoken feedback is processed into anormalized set of quality of life measures which each relate to uniformself-assessment indicators, as further described below with reference toFIG. 15. Alternatively, in a further embodiment of the system 200, thepatient 11 can provide spoken feedback via a telephone network 203 usinga standard telephone 203, including a conventional wired telephone or awireless telephone, such as a cellular telephone, as further describedbelow with reference to FIG. 20. In the described embodiment, themicrophone 201 and the speaker 202 are standard, off-the-shelfcomponents commonly included with consumer personal computer systems, asis known in the art.

[0086] The system 200 continuously monitors and collects sets of devicemeasures from the implantable medical device 12. To augment the on-goingmonitoring process with a patient's self-assessment of physical andemotional well-being, a quality of life measures set can be recorded bythe remote client 18 Importantly, each quality of life measures set isrecorded substantially contemporaneous to the collection of anidentified collected device measures set. The date and time of day atwhich the quality of life measures set was recorded can be used tocorrelate the quality of life measures set to the collected devicemeasures set recorded closest in time to the quality of life measuresset. The pairing of the quality of life measures set and an identifiedcollected device measures set provides medical practitioners with a morecomplete picture of the patient's medical status by combiningphysiological “hard” machine-recorded data with semi-quantitative “soft”patient-provided data.

[0087]FIG. 13 is a block diagram showing the software modules of theremote client 18 of the system 200 of FIG. 12. As with the softwaremodules of the system 10 of FIG. 1, each module here is also a computerprogram written as source code in a conventional programming language,such as the C or Java programming languages, and is presented forexecution by the CPU as object or byte code, as is known in the arts.There are two basic software modules, which functionally define theprimary operations performed by the remote client 18 in providingnormalized voice feedback: audio prompter 210 and speech engine 214. Theremote client 18 includes a secondary storage 219, such as a hard drive,a CD ROM player, and the like, within which is stored data used by thesoftware modules. Conceptually, the voice reproduction and recognitionfunctions performed by the audio prompter 210 and speech engine 214 canbe described separately, but those same functions could also beperformed by a single voice processing module, as is known in the art.

[0088] The audio prompter 210 generates voice prompts 226 which areplayed back to the patient 11 on the speaker 202. Each voice prompt isin the form of a question or phrase seeking to develop a self-assessmentof the patient's physical and emotional well being. For example, thepatient 11 might be prompted with, “Are you short of breath?” The voiceprompts 226 are either from a written script 220 reproduced by speechsynthesizer 211 or pre-recorded speech 221 played back by playbackmodule 212. The written script 220 is stored within the secondarystorage 219 and consists of written quality of life measure requests.Similarly, the pre-recorded speech 221 is also stored within thesecondary storage 219 and consists of sound “bites” of recorded qualityof life measure requests in either analog or digital format.

[0089] The speech engine 214 receives voice responses 227 spoken by thepatient 11 into the microphone 201. The voice responses 227 can beunstructured, natural language phrases and sentences. A voice grammar222 provides a lexical structuring for use in determining the meaning ofeach spoken voice response 227. The voice grammar 222 allows the speechengine 214 to “normalize” the voice responses 227 into recognizedquality of life measures 228. Individual spoken words in each voiceresponse 227 are recognized by a speech recognition module 215 andtranslated into written words. In turn, the written words are parsedinto tokens by a parser 216. A lexical analyzer 217 analyzes the tokensas complete phrases in accordance with a voice grammar 222 stored withinthe secondary storage 219. Finally, if necessary, the individual wordsare normalized to uniform terms by a lookup module 218 which retrievessynonyms maintained as a vocabulary 223 stored within the secondarystorage 218. For example, in response to the query, “Are you short ofbreath?,” a patient might reply, “I can hardly breath,” “I am panting,”or “I am breathless.” The speech recognition module 215 would interpretthese phrases to imply dyspnea with a corresponding quality of lifemeasure indicating an awareness by the patient of abnormal breathing. Inthe described embodiment, the voice reproduction and recognitionfunctions can be performed by the various natural voice softwareprograms licensed by Dragon Systems, Inc., Newton, Mass. Alternatively,the written script 220 voice grammar 222, and vocabulary 223 could beexpressed as a script written in a voice page markup language forinterpretation by a voice browser operating on the remote client 18. Twoexemplary voice page description languages include the VoxML markuplanguage, licensed by Motorola, Inc., Chicago, Ill., and described athttp://www.voxml.com, and the Voice extensible Markup Language (VXML),currently being jointly developed by AT&T, Motorola, LucentTechnologies, and IBM, and described at http://www.vxmlforum.com. Themodule functions are further described below in more detail beginningwith reference to FIGS. 16A-16B.

[0090]FIG. 14 is a block diagram showing the software modules of theserver system 16 of the system 200 of FIG. 12. The database module 51,previously described above with reference to FIG. 3, also receives thecollected quality of life measures set 228 from the remote client 18,which the database module 51 stores into the appropriate patient carerecord in the database 52. The date and time of day 236 (shown in FIG.15) of the quality of life measures set 228 is matched to the date andtime of day 73 (shown in FIG. 5) of the collected measures set 50recorded closest in time to the quality of life measures set 228. Thematching collected measures set 50 is identified in the patient carerecord and can be analyzed with the quality of life measures set 228 bythe analysis module 53, such as described above with reference to FIG.8.

[0091]FIG. 15 is a database schema showing, by way of example, theorganization of a quality of life record 230 for cardiac patient carestored as part of a patient care record in the database 17 of the system200 of FIG. 12. A quality of life score is a semi-quantitativeself-assessment of an individual patient's physical and emotional wellbeing. Non-commercial, non-proprietary standardized automated quality oflife scoring systems are readily available, such as provided by the DukeActivities Status Indicator. For example, for a cardiac patient, thequality of life record 230 stores the following information: healthwellness 231, shortness of breath 232, energy level 233, chestdiscomfort 235, time of day 234, and other quality of life measures aswould be known to one skilled in the art. Other types of quality of lifemeasures are possible.

[0092] A quality of life indicator is a vehicle through which a patientcan remotely communicate to the patient care system how he or she issubjectively feeling. The quality of life indicators can includesymptoms of disease. When tied to machine-recorded physiologicalmeasures, a quality of life indicator can provide valuable additionalinformation to medical practitioners and the automated collection andanalysis patient care system 200 not otherwise discernible withouthaving the patient physically present. For instance, a scoring systemusing a scale of 1.0 to 10.0 could be used with 10.0 indicating normalwellness and 1.0 indicating severe health problems. Upon the completionof an initial observation period, a patient might indicate a healthwellness score 231 of 5.0 and a cardiac output score of 5.0. After onemonth of remote patient care, the patient might then indicate a healthwellness score 231 of 4.0 and a cardiac output score of 4.0 and a weeklater indicate a health wellness score 231 of 3.5 and a cardiac outputscore of 3.5. Based on a comparison of the health wellness scores 231and the cardiac output scores, the system 200 would identify a trendindicating the necessity of potential medical intervention while acomparison of the cardiac output scores alone might not lead to the sameprognosis.

[0093] FIGS. 16A-16B are flow diagrams showing a method 239 forproviding normalized voice feedback from an individual patient 11 in anautomated collection and analysis patient care system 200. As with themethod 90 of FIG. 7, this method is also implemented as a conventionalcomputer program and performs the same set of steps as described withreference to FIG. 7 with the following additional functionality. First,voice feedback spoken by the patient 11 into the remote client 18 isprocessed into a quality of life measures set 228 (block 240), asfurther described below with reference to FIG. 17. The voice feedback isspoken substantially contemporaneous to the collection of an identifieddevice measures set 50. The appropriate collected device measures set 50can be matched to and identified with (not shown) the quality of lifemeasures set 228 either by matching their respective dates and times ofday or by similar means, either by the remote client 18 or the serversystem 16. The quality of life measures set 228 and the identifiedcollected measures set 50 are sent over the internetwork 15 to theserver system 16 (block 241). Note the quality of life measures set 228and the identified collected measures set 50 both need not be sent overthe internetwork 15 at the same time, so long as the two sets areultimately paired based on, for example, date and time of day. Thequality of life measures set 228 and the identified collected measuresset 50 are received by the server system 16 (block 242) and stored inthe appropriate patient care record in the database 52 (block 243).Finally, the quality of life measures set 228, identified collectedmeasures set 50, and one or more collected measures sets 50 are analyzed(block 244) and feedback, including a patient status indicator 54 (shownin FIG. 14), is provided to the patient (block 245).

[0094]FIG. 17 is a flow diagram showing the routine for processing voicefeedback 240 for use in the method of FIGS. 16A-16B. The purpose of thisroutine is to facilitate a voice interactive session with the patient 11during which is developed a normalized set of quality of life measures.Thus, the remote client 18 requests a quality of life measure via avoice prompt (block 250), played on the speaker 202 (shown in FIG. 13),as further described below with reference to FIG. 18. The remote client18 receives the spoken feedback from the patient 11 (block 251) via themicrophone 201 (shown in FIG. 13). The remote client 18 recognizesindividual words in the spoken feedback and translates those words intowritten words (block 252), as further described below with reference toFIG. 19. The routine returns at the end of the voice interactivesession.

[0095]FIG. 18 is a flow diagram showing the routine for requesting aquality of life measure 251 for use in the routine 240 of FIG. 17. Thepurpose of this routine is to present a voice prompt 226 to the user viathe speaker 202. Either pre-recorded speech 221 or speech synthesizedfrom a written script 220 can be used. Thus, if synthesized speech isemployed by the remote client 18 (block 260), a written script, such asa voice markup language script, specifying questions and phrases whichwith to request quality of life measures is stored (block 261) on thesecondary storage 219 of the remote client 18. Each written quality oflife measure request is retrieved by the remote client 18 (block 262)and synthesized into speech for playback to the patient 11 (block 263).Alternatively, if pre-recorded speech is employed by the remote client18 (block 260), prerecorded voice “bites” are stored (block 264) on thesecondary storage 219 of the remote client 18. Each pre-recorded qualityof life measure request is retrieved by the remote client 18 (block 265)and played back to the patient 11 (block 266). The routine then returns.

[0096]FIG. 19 is a flow diagram showing the routine for recognizing andtranslating individual spoken words 252 for use in the routine 240 ofFIG. 17. The purpose of this routine is to receive and interpret afree-form voice response 227 from the user via the microphone 201.First, a voice grammar consisting of a lexical structuring of words,phrases, and sentences is stored (block 270) on the secondary storage219 of the remote client 18. Similarly, a vocabulary of individual wordsand their commonly accepted synonyms is stored (block 271) on thesecondary storage 219 of the remote client 18. After individual words inthe voice feedback are recognized (block 272), the individual words areparsed into tokens (block 273). The voice feedback is then lexicallyanalyzed using the tokens and in accordance with the voice grammar 222(block 274) to determine the meaning of the voice feedback. Ifnecessary, the vocabulary 223 is referenced to lookup synonyms of theindividual words (block 275). The routine then returns.

[0097]FIG. 20 is a block diagram showing the software modules of theserver system in a further embodiment of the system 200 of FIG. 12. Thefunctionality of the remote client 18 in providing normalized voicefeedback is incorporated directly into the server system 16. The system200 of FIG. 12 requires the patient 11 to provide spoken feedback via alocally situated remote client 18. However, the system 280 enables apatient 11 to alternatively provide spoken feedback via a telephonenetwork 203 using a standard telephone 203, including a conventionalwired telephone or a wireless telephone, such as a cellular telephone.The server system 16 is augmented to include the audio prompter 210, thespeech engine 214, and the data stored in the secondary storage 219. Atelephonic interface 280 interfaces the server system 16 to thetelephone network 203 and receives voice responses 227 and sends voiceprompts 226 to and from the server system 16. Telephonic interfacingdevices are commonly known in the art.

[0098]FIG. 21 is a block diagram showing a system for providingnormalized voice feedback from an individual patient in an automatedcollection and analysis patient care system 300 in accordance with afurther embodiment of the present invention. The system 300 providesremote patient care in a manner similar to the system 200 of FIG. 12,but with additional functionality for diagnosing and monitoring multiplesites within a patient's body using a variety of patient sensors fordiagnosing one or more disorder. The patient 301 can be the recipient ofan implantable medical device 302, as described above, or have anexternal medical device 303 attached, such as a Holter monitor-likedevice for monitoring electrocardiograms. In addition, one or more sitesin or around the patient's body can be monitored using multiple sensors304 a, 304 b, such as described in U.S. Pat. Nos. 4,987,897; 5,040,536;5,113,859; and 5,987,352, the disclosures of which are incorporatedherein by reference. One automated system and method for collecting andanalyzing retrieved patient information suitable for use with thepresent invention is described in the related, commonly-owned U.S.patent application, Ser. No. 09/476,602, pending, filed Dec. 31, 1999,the disclosure of which is incorporated herein by reference. Other typesof devices with physiological measure sensors, both heterogeneous andhomogenous, could be used, either within the same device or working inconjunction with each other, as is known in the art.

[0099] As part of the system 300, the database 17 stores patient carerecords 305 for each individual patient to whom remote patient care isbeing provided. Each patient care record 305 contains normal patientidentification and treatment profile information, as well as medicalhistory, medications taken, height and weight, and other pertinent data(not shown). The patient care records 305 consist primarily ofmonitoring sets 306 storing device and derived measures (D&DM) sets 307and quality of life and symptom measures (QOLM) sets 308 recorded anddetermined thereafter on a regular, continuous basis. The organizationof the device and derived measures sets 305 for an exemplary cardiacpatient care record is described above with reference to FIG. 5. Theorganization of the quality of life and symptom measures sets 308 isfurther described below with reference to FIG. 23.

[0100] Optionally, the patient care records 305 can further include areference baseline 309 storing a special set of device and derivedreference measures sets 310 and quality of life and symptom measuressets 311 recorded and determined during an initial observation period,such as described in the related, commonly-owned U.S. patentapplication, Ser. No. 09/476,601, pending, filed Dec. 31, 1999, thedisclosure of which is incorporated herein by reference. Other forms ofdatabase organization are feasible.

[0101] Finally, simultaneous notifications can also be delivered to thepatient's physician, hospital, or emergency medical services provider312 using feedback means similar to that used to notify the patient. Asdescribed above, the feedback could be by electronic mail or byautomated voice mail or facsimile. Furthermore, the spoken voicefeedback from the patient and the feedback provided by the system 200can be communicated by means of or in combination with the medicaldevice itself, whether implantable, external or otherwise.

[0102]FIG. 22 is a block diagram showing the analysis module 53 of theserver system 16 of FIG. 21. The peer collected measures sets 60 andsibling collected measures sets 61 can be organized into site specificgroupings based on the sensor from which they originate, that is,implantable medical device 302, external medical device 303, or multiplesensors 304 a, 304 b. The functionality of the analysis module 53 isaugmented to iterate through a plurality of site specific measures sets315 and one or more disorders.

[0103] As described above, as an adjunct to remote patient care throughthe monitoring of measured physiological data via implantable medicaldevice 302, external medical device 303 and multiple sensors 304 a, 304b, quality of life and symptom measures sets 308 can also be stored inthe database 17 as part of the monitoring sets 306. A quality of lifemeasure is a semi-quantitative self-assessment of an individualpatient's physical and emotional well-being and a record of symptoms,such as provided by the Duke Activities Status Indicator. These scoringsystems can be provided for use by the patient 11 on the personalcomputer 18 (shown in FIG. 1) to record his or her quality of lifescores for both initial and periodic download to the server system 16.

[0104]FIG. 23 is a database schema which augments the database schemadescribed above with reference to FIG. 15 and showing, by way ofexample, the organization of a quality of life and symptom measures setrecord 320 for care of patients stored as part of a patient care record305 in the database 17 of the system 300 of FIG. 21. The followingexemplary information is recorded for a patient: overall health wellness321, psychological state 322, chest discomfort 323, location of chestdiscomfort 324, palpitations 325, shortness of breath 326, exercisetolerance 327, cough 328, sputum production 329, sputum color 330,energy level 331, syncope 332, near syncope 333, nausea 334, diaphoresis335, time of day 91, and other quality of life and symptom measures aswould be known to one skilled in the art.

[0105] Other types of quality of life and symptom measures are possible,such as those indicated by responses to the Minnesota Living with HeartFailure Questionnaire described in E. Braunwald, ed., “Heart Disease—ATextbook of Cardiovascular Medicine,” pp. 452-454, W. B. Saunders Co.(1997), the disclosure of which is incorporated herein by reference.Similarly, functional classifications based on the relationship betweensymptoms and the amount of effort required to provoke them can serve asquality of life and symptom measures, such as the New York HeartAssociation (NYHA) classifications I, II, III and IV, also described inIbid.

[0106] The patient may also add non-device quantitative measures, suchas the six-minute walk distance, as complementary data to the device andderived measures sets 307 and the symptoms during the six-minute walk toquality of life and symptom measures sets 308.

[0107]FIG. 24 is a record view showing, by way of example, a set ofpartial cardiac patient care records stored in the database 17 of thesystem 300 of FIG. 21. Three patient care records are again shown forPatient 1, Patient 2, and Patient 3 with each of these recordscontaining site specific measures sets 315, grouped as follows. First,the patient care record for Patient 1 includes three site specificmeasures sets A, B and C, corresponding to three sites on Patient 1'sbody. Similarly, the patient care record for Patient 2 includes two sitespecific measures sets A and B, corresponding to two sites, both ofwhich are in the same relative positions on Patient 2's body as thesites for Patient 1. Finally, the patient care record for Patient 3includes two site specific measures sets A and D, also corresponding totwo medical device sensors, only one of which, Site A, is in the samerelative position as Site A for Patient 1 and Patient 2.

[0108] The analysis module 53 (shown in FIG. 22) performs two furtherforms of comparison in addition to comparing the individual measures fora given patient to other individual measures for that same patient or toother individual measures for a group of other patients sharing the samedisease-specific characteristics or to the patient population ingeneral. First, the individual measures corresponding to each body sitefor an individual patient can be compared to other individual measuresfor that same patient, a peer group or a general patient population.Again, these comparisons might be peer-to-peer measures projected overtime, for instance, comparing measures for each site, A, B and C, forPatient 1, X_(n) _(A) , X_(n′) _(A) , X_(n″) _(A) , X_(n−1) _(A) ,X_(n−1′) _(A) , X_(n−1″) _(A) , X_(n−2) _(A) , X_(n−2′) _(A) , X_(n−2″)_(A) . . . X₀ _(A) , X_(0′) _(A) , X_(0″) _(A) ; X_(n) _(B) , X_(n′)_(B) , X_(n″) _(B) , X_(n−1) _(B) , X_(n−1′) _(B) , X_(n−1″) _(B) ,X_(n−2) _(B) , X_(n−2′) _(B) , X_(n−2″) _(B) . . . X₀ _(B) , X_(0′) _(B), X_(0″) _(B) ; X_(n) _(C) , X_(n′) _(C) , X_(n″) _(C) , X_(n−1) _(C) ,X_(n−1′) _(C) , X_(n−1″) _(C) , X_(n−2) _(C) , X_(n−2′) _(C) , X_(n−2″)_(C) . . . X₀ _(C) , X_(0′) _(C) , X_(0″) _(C) ; comparing comparablemeasures for Site A for the three patients, X_(n) _(A) , X_(n′) _(A) ,X_(n″) _(A) , X_(n−1) _(A) , X_(n−1′) _(A) , X_(n−1″) _(A) , X_(n−2)_(A) , X_(n−2′) _(A) , X_(n−2″) _(A) . . . X₀ _(A) , X_(0′) _(A) ,X_(0″) _(A) ; or comparing the individual patient's measures to anaverage from the group. Similarly, these comparisons might besibling-to-sibling measures for single snapshots, for instance,comparing comparable measures for Site A for the three patients, X_(n)_(A) , X_(n′) _(A) , X_(n″) _(A) , Y_(n) _(A) , Y_(n′) _(A) , Y_(n″)_(A) , and Z_(n) _(A) , Z_(n′) _(A) , Z_(n″) _(A) , or comparing thosesame comparable measures for Site A projected over time, for instance,X_(n) _(A) , X_(n′) _(A) , X_(n″) _(A) , Y_(n) _(A) , Y_(n′) _(A) ,Y_(n″) _(A) , Z_(n) _(A) , Z_(n′) _(A) , Z_(n″) _(A) , X_(n−1) _(A) ,X_(n−1′) _(A) , X_(n−1″) _(A) , Y_(n−1) _(A) , Y_(n−1′) _(A) , Y_(n−1″)_(A) , Z_(n−1) _(A) , Z_(n−1′) _(A) , Z_(n−1″) _(A) , X_(n−2) _(A) ,X_(n−2′) _(A) , X_(n−2″) _(A) , Y_(n−2) _(A) , Y_(n−2′) _(A) , Y_(n−2″)_(A) , Z_(n−2) _(A) , Z_(n−2′) _(A) , Z_(n−2″) _(A) . . . X₀ _(A) ,X_(0′) _(A) , X_(0″) _(A) , Y₀ _(A) , Y_(0′) _(A) , Y_(0″) _(A) , and Z₀_(A) , Z_(0′) _(A) , Z_(0″) _(A) . Other forms of site-specificcomparisons, including comparisons between individual measures fromnon-comparable sites between patients, are feasible.

[0109] Second, the individual measures can be compared on a disorderspecific basis. The individual measures stored in each cardiac patientrecord can be logically grouped into measures relating to specificdisorders and diseases, for instance, congestive heart failure,myocardial infarction, respiratory distress, and atrial fibrillation.The foregoing comparison operations performed by the analysis module 53are further described below with reference to FIGS. 26A-26B.

[0110]FIG. 25 is a Venn diagram showing, by way of example, peer groupoverlap between the partial patient care records 305 of FIG. 24. Eachpatient care record 305 includes characteristics data 350, 351, 352,including personal traits, demographics, medical history, and relatedpersonal data, for patients 1, 2 and 3, respectively. For example, thecharacteristics data 350 for patient 1 might include personal traitswhich include gender and age, such as male and an age between 40-45; ademographic of resident of New York City; and a medical historyconsisting of anterior myocardial infraction, congestive heart failureand diabetes. Similarly, the characteristics data 351 for patient 2might include identical personal traits, thereby resulting in partialoverlap 353 of characteristics data 350 and 351. Similar characteristicsoverlap 354, 355, 356 can exist between each respective patient. Theoverall patient population 357 would include the universe of allcharacteristics data. As the monitoring population grows, the number ofpatients with personal traits matching those of the monitored patientwill grow, increasing the value of peer group referencing. Large peergroups, well matched across all monitored measures, will result in awell known natural history of disease and will allow for more accurateprediction of the clinical course of the patient being monitored. If thepopulation of patients is relatively small, only some traits 356 will beuniformly present in any particular peer group. Eventually, peer groups,for instance, composed of 100 or more patients each, would evolve underconditions in which there would be complete overlap of substantially allsalient data, thereby forming a powerful core reference group for anynew patient being monitored.

[0111] FIGS. 26A-26B are flow diagrams showing a method for providingnormalized voice feedback from an individual patient in an automatedcollection and analysis patient care system 360 in accordance with afurther embodiment of the present invention. As with the method 239 ofFIGS. 16A and 16B, this method is also implemented as a conventionalcomputer program and performs the same set of steps as described withreference to FIGS. 16A and 16B with the following additionalfunctionality. As before, the patient care records are organized in thedatabase 17 with a unique patient care record assigned to eachindividual patient (block 361). Next, the individual measures for eachsite are iteratively obtained in a first processing loop (blocks362-367) and each disorder is iteratively analyzed in a secondprocessing loop (blocks 368-370). Other forms of flow control arefeasible, including recursive processing.

[0112] During each iteration of the first processing loop (blocks362-367), the collected measures sets for an individual patient areretrieved from the medical device or sensor located at the current site(block 363) using a programmer, interrogator, telemetered signalstransceiver, and the like. The retrieved collected measures sets aresent, on a substantially regular basis, over the internetwork 15 orsimilar communications link (block 364) and periodically received by theserver system 16 (block 365). The collected measures sets are storedinto the patient care record 305 in the database 17 for that individualpatient (block 366). Any voice feedback spoken by the patient 11 intothe remote client 18 is processed into a quality of life measures set228 (block 240), as described above with reference to FIG. 17. The voicefeedback is spoken substantially contemporaneous to the collection of anidentified device measures set 50. The appropriate collected devicemeasures set 50 can be matched to and identified with (not shown) thequality of life measures set 228 either by matching their respectivedates and times of day or by similar means, either by the remote clientor the server system 16. The quality of life measures set 228 and theidentified collected measures set 50 are sent over the internetwork 15to the server system 16 (block 241). The quality of life measures set228 and the identified collected measures set 50 are received by theserver system 16 (block 242) and stored in the appropriate patient carerecord in the database 52 (block 243).

[0113] During each iteration of the second processing loop (blocks368-370), the quality of life measures set 228, identified collectedmeasures set 50, and one or more of the collected measures sets for thatpatient are analyzed for the current disorder are analyzed (block 244).Finally, feedback based on the analysis is sent to that patient over theinternetwork 15 as an email message, via telephone line as an automatedvoice mail or facsimile message, or by similar feedback communicationslink (block 245). In addition, the measures sets can be furtherevaluated and matched to diagnose specific medical disorders, such ascongestive heart failure, myocardial infarction, respiratory distress,and atrial fibrillation, as described in related, commonly-owned U.S.patent applications, Ser. No. 09/441,623, pending, filed Nov. 16, 1999;Ser. No. 09/441,612, pending, filed Nov. 16, 1999; Ser. No. 09/442,125,pending, filed Nov. 16, 1999; and Ser. No. 09/441,613, pending, filedNov. 16, 1999, the disclosures of which are incorporated herein byreference. In addition, multiple near-simultaneous disorders can beordered and prioritized as part of the patient status indicator asdescribed in the related, commonly-owned U.S. patent application, Ser.No. 09/441,405, pending, filed Nov. 16, 1999, the disclosure of which isincorporated herein by reference.

[0114] Therefore, through the use of the collected measures sets, thepresent invention makes possible immediate access to expert medical careat any time and in any place. For example, after establishing andregistering for each patient an appropriate baseline set of measures,the database server could contain a virtually up-to-date patienthistory, which is available to medical providers for the remotediagnosis and prevention of serious illness regardless of the relativelocation of the patient or time of day.

[0115] Moreover, the gathering and storage of multiple sets of criticalpatient information obtained on a routine basis makes possible treatmentmethodologies based on an algorithmic analysis of the collected datasets. Each successive introduction of a new collected measures set intothe database server would help to continually improve the accuracy andeffectiveness of the algorithms used. In addition, the present inventionpotentially enables the detection, prevention, and cure of previouslyunknown forms of disorders based on a trends analysis and by across-referencing approach to create continuously improving peer-groupreference databases.

[0116] Similarly, the present invention makes possible the provision oftiered patient feedback based on the automated analysis of the collectedmeasures sets. This type of feedback system is suitable for use in, forexample, a subscription based health care service. At a basic level,informational feedback can be provided by way of a simple interpretationof the collected data. The feedback could be built up to provide agradated response to the patient, for example, to notify the patientthat he or she is trending into a potential trouble zone. Humaninteraction could be introduced, both by remotely situated and localmedical practitioners. Finally, the feedback could include directinterventive measures, such as remotely reprogramming a patient's IPG.

[0117] Finally, the present invention allows “live” patient voicefeedback to be captured simultaneously with the collection ofphysiological measures by their implantable medical device. The voicefeedback is normalized to a standardized set of quality of life measureswhich can be analyzed in a remote, automated fashion. The voice feedbackcould also be coupled with visual feedback, such as through digitalphotography or video, to provide a more complete picture of thepatient's physical well-being.

[0118] While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention.

What is claimed is:
 1. A system for processing normalized voice feedbackfor use in automated patient care, comprising: a database storingpatient care records with each record containing one or morephysiological measures relating to individual patient information andone or more quality of life measures relating to normalized spokenpatient self-assessment indicators, each quality of life measurerecorded substantially contemporaneous to the physiological measures;and an analysis module analyzing the physiological measures and thecontemporaneously recorded quality of life measures retrieved from onesuch patient care record to determine a patient status.
 2. A systemaccording to claim 1 , further comprising: a medical device having asensor for monitoring and recording the physiological measures from ananatomical site at least one of directly and derivatively.
 3. A systemaccording to claim 2 , further comprising: at least one further sensormonitoring and recording the physiological measures from an anatomicalsite unique from the anatomical site monitored by any other such sensor;and the analysis module determining the patient status by comparingphysiological measures retrieved from one such patient care record for aplurality of sensors.
 4. A system according to claim 1 , furthercomprising: the database storing patient care records with each recordcontaining physiological measures from sensors monitoring a plurality ofanatomical sites within the individual patient; and the analysis moduleanalyzing the physiological measures from one such patient care recordrelative to each anatomical site.
 5. A system according to claim 1 , theremote client further comprising: a feedback device interfacing andrecording the quality of life measures from the individual patient.
 6. Amethod for processing normalized voice feedback for use in automatedpatient care, comprising: retrieving one or more physiological measuresrelating to individual patient information and one or more quality oflife measures relating to normalized spoken patient self-assessmentindicators from a patient care record, each quality of life measurerecorded substantially contemporaneous to the physiological measures;and analyzing the physiological measures and the contemporaneouslyrecorded quality of life measures retrieved from one such patient carerecord to determine a patient status.
 7. A method according to claim 6 ,further comprising: obtaining the physiological measures from a medicaldevice having a sensor for monitoring the physiological measures from ananatomical site at least one of directly and derivatively.
 8. A methodaccording to claim 7 , further comprising: obtaining the physiologicalmeasures monitored by at least one further sensor monitoring thephysiological measures from an anatomical site unique from theanatomical site monitored by any other such sensor; and determining thepatient status by comparing physiological measures retrieved from onesuch patient care record for a plurality of sensors.
 9. A methodaccording to claim 6 , further comprising: receiving the physiologicalmeasures from sensors monitoring a plurality of anatomical sites withinthe individual patient from a patient care record; and analyzing thephysiological measures relative to each anatomical site.
 10. A methodaccording to claim 6 , the remote client further comprising: interfacingand recording the quality of life measures from the individual patient.11. A computer-readable storage medium for a device holding code forperforming the method according to claims 6, 7, 9, and
 10. 12. A systemfor soliciting normalized voice feedback for use in automated patientcare, comprising: a medical device having a sensor for monitoring andrecording one or more physiological measures relating to individualpatient information from an anatomical site at least one of directly andderivatively; a feedback device interfacing and recording one or morequality of life measures relating to normalized spoken patientself-assessment indicators, each quality of life measure recordedsubstantially contemporaneous to the physiological measures; and adatabase storing patient care records with each record containing thephysiological measures and the quality of life measures.
 13. A systemaccording to claim 13 , the remote client further comprising: ananalysis module analyzing the physiological measures and thecontemporaneously recorded quality of life measures retrieved from onesuch patient care record to determine a patient status.
 14. A systemaccording to claim 13 , further comprising: the analysis moduleanalyzing the physiological measures from sensors monitoring a pluralityof anatomical sites within an individual patient relative to eachanatomical site.
 15. A system according to claim 13 , furthercomprising: written script comprising one or more quality of lifemeasure prompts each corresponding to one such quality of life measure;and the feedback device synthesizing speech from the written script andinteracting with the individual patient during recordation of thequality of life measures.
 16. A system according to claim 13 , furthercomprising: pre-recorded speech comprising one or more quality of lifemeasure prompts each corresponding to one such quality of life measure;and the feedback device playing the pre-recorded speech and interactingwith the individual patient during recordation of the quality of lifemeasures.
 17. A system according to claim 13 , the remote client furthercomprising: the feedback device converting speech spoken by theindividual patient into the quality of life measures.
 18. A method forsoliciting normalized voice feedback for use in automated patient care,comprising: obtaining one or more physiological measures relating toindividual patient information from a medical device having a sensor formonitoring and recording from an anatomical site at least one ofdirectly and derivatively; recording one or more quality of lifemeasures relating to normalized spoken patient self-assessmentindicators, each quality of life measure recorded substantiallycontemporaneous to the physiological measures; and storing thephysiological measures and the quality of life measures in patient carerecords.
 19. A method according to claim 18 , the remote client furthercomprising: analyzing the physiological measures and thecontemporaneously recorded quality of life measures retrieved from onesuch patient care record to determine a patient status.
 20. A methodaccording to claim 18 , further comprising: analyzing the physiologicalmeasures from sensors monitoring a plurality of anatomical sites withinan individual patient relative to each anatomical site.
 21. A methodaccording to claim 18 , further comprising: defining a written scriptcomprising one or more quality of life measure prompts eachcorresponding to one such quality of life measure; and synthesizingspeech from the written script and interacting with the individualpatient during recordation of the quality of life measures.
 22. A methodaccording to claim 18 , further comprising: storing pre-recorded speechcomprising one or more quality of life measure prompts eachcorresponding to one such quality of life measure; and playing thepre-recorded speech and interacting with the individual patient duringrecordation of the quality of life measures.
 23. A method according toclaim 18 , the remote client further comprising: converting speechspoken by the individual patient into the quality of life measures. 24.A computer-readable storage medium for a device holding code forperforming the method according to claims 18, 19, 20, 21, 22 and 23.